With the clinical environment becoming more data-reliant, healthcare professionals now have unparalleled access to comprehensive clinical information from numerous sources. Then, one of the main issues is how to avoid overloading practitioners with large amounts of (irrelevant) information while guiding them to the relevant documents for specific patient cases. Additional challenges appear due to the shortness of queries and the presence of long (and maybe noisy) contextual information. This demo presents Re2Dan, a web Retrieval and recommender of Danish medical documents. Re2Dan leverages several techniques to improve the quality of retrieved documents. First, it combines lexical and semantic searches to understand the meaning and context of user queries, allowing the retrieval of documents that are conceptually similar to the user’s query. Second, it recommends similar queries, allowing users to discover related documents and insights. Third, when given contextual information (e.g., from patients’ clinical notes), it suggests medical concepts to expand the user query, enabling a more focused search scope and thus obtaining more accurate recommendations. Preliminary analyses showed the effectiveness of the recommender in improving the relevance and comprehensiveness of recommendations, thereby assisting healthcare professionals in finding relevant information for informed decision-making.